bioRxiv preprint first posted online Jun. 17, 2016; doi: http://dx.doi.org/10.1101/059477. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
Botrytis cinerea loss and restoration of virulence during in vitro culture follows flux in
global DNA methylation
James Breen1,2, Luis Alejandro Jose Mur3, Anushen Sivakumaran3, Aderemi Akinyemi3, Michael
James Wilkinson3, Carlos Marcelino Rodriguez Lopez4*
1
Robinson Research Institute, University of Adelaide, Adelaide SA 5005
2
University of Adelaide Bioinformatics Hub, School of Biological Sciences, University of
Adelaide, Adelaide SA 5005
3
Pwllpeiran Upland Research Centre, Institute of Biological, Environmental and Rural Sciences,
Edward Llywd Building, Penglais Campus, Aberystwyth, Ceredigion, SY23 3FG, UK.
4
Environmental Epigenetics and Genetics Group, School of Agriculture, Food and Wine, Waite
Research Precinct, University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia.
*Corresponding author
Telephone: +61 (0) 831 30774
Email: [email protected]
Total word count: 6499
Introduction: 764
Materials and Methods: 1735
Results: 1798
Discussion: 2202
Figures: 6 (Figures 1-6 should be published in colour)
Tables: 3
Supporting information: 8 supplementary tables and 2 supplementary figures (both to be
published in colour)
bioRxiv preprint first posted online Jun. 17, 2016; doi: http://dx.doi.org/10.1101/059477. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
Summary

Pathogenic fungi can lose virulence after protracted periods of culture but little is known of the
underlying mechanisms. Here we present the first single-base resolution methylome for the
plant pathogen B. cinerea and identify differentially methylated genes/genomic regions
associated with virulence erosion.

Cultures were maintained for eight months with subcultures and virulence testing every month.
Methylation-sensitive amplified polymorphisms were performed at monthly intervals to
characterise global changes to the pathogen's genome during culture and also on DNA from
mycelium inoculated onto Arabidopsis thaliana after eight months in culture. Characterisation
of culture-induced epialleles was assessed by whole-genome re-sequencing and whole-genome
bisulfite sequencing.

Virulence declined with time in culture and recovered after inoculation on A. thaliana.
Variation detected by methylation-sensitive amplified polymorphisms followed virulence
changes during culture. Whole-genome (bisulfite) sequencing showed marked changes on
global and local methylation during culture but no significant genetic changes.

We imply that virulence is a non-essential plastic character that is at least partly modified by
changing levels of DNA methylation during culture. We hypothesise that changing DNA
methylation during culture may be responsible for the high virulence/low virulence transition in
B. cinerea and speculate that this may offer fresh opportunities to control pathogen virulence.
bioRxiv preprint first posted online Jun. 17, 2016; doi: http://dx.doi.org/10.1101/059477. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
Introduction
Botrytis cinerea is a pathogenic ascomycete responsible for grey mould on a diversity of plant
tissue types across hundreds of dicotyledonous plant species (Fournier et al., 2013). It is
estimated that this fungus causes annual losses of up to $100 billion worldwide (Weiberg et al.,
2013). The wide variety of tissues and species infected by B. cinerea suggests it is highly plastic
in its ability to penetrate a host thanks to a large ‘arsenal of weapons’ at its disposal. B. cinerea is
a capable saprotroph and necrotroph, with different genetic types often showing a trade-off
between saprotrophic and necrotrophic capabilities (Martinez et al., 2005). As with other
pathogens, B. cinerea undergoes complex transcriptional and developmental regulation to
orchestrate interactions with its host. However, virulence levels of B. cinerea strains are not
necessarily a fixed feature. For example, virulence has been observed to diminish during
protracted in vitro culture (Pathirana et al., 2009). Degenerated cultures have been reported in a
wide range of pathogenic fungi (Butt et al., 2006), although very little is known about why
cultures lose virulence. Several factors have been postulated as possible causes of virulence
erosion including dsRNA mycoviruses (Chu et al., 2002; Castro et al., 2003), loss of conditional
dispensable chromosomes (Akamatsu et al., 1999; Hatta et al., 2002) or culture-induced
selection of nonvirulent strains. However, it is difficult to reconcile mycoviruses infection or
chromosome loss as possible causes with a characteristic common to almost all in vitro-derived
non-virulent fungal strains; that virulence is restored after one passage on their host (Butt et al.,
2006). Furthermore, cultured fungal strains lose virulence irrespective of whether the parent
culture originated from a single or multiple-spore colony (Butt et al., 2006), discounting the
additional possibility of culture-induced selection favouring nonvirulent strains.
The reversibility of this aspect of the observed phenotype comes in response to changes in the
growing environment and so should be viewed as a plastic feature of the pathogen (Kelly et al.,
2012). In 1942 C.H. Waddington first proposed the term epigenotype to describe the interface
between genotype and phenotype. Since then, a large body of research has been carried out to
better understand the role of epigenetic regulatory systems in shaping the phenotype of higher
organisms in fluctuating environments (Geyer et al., 2011; Tricker et al., 2012). Epigenetic
processes operate in a number of ways to alter the phenotype without altering genetic code (Bird,
2007). These include DNA methylation, histone modifications, and mRNA editing and
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degradation by noncoding RNAs. Such processes are intimately entwined and often work in a
synergistic way to achieve changes in phenotype (Rodríguez López & Wilkinson, 2015). DNA
methylation, and more specifically cytosine methylation (i.e. the incorporation of a methyl group
to carbon 5 of the cytosine pyrimidine ring (5-mC)) is the most studied epigenetic mechanism. It
is present across many eukaryotic phyla, including plants, mammals, birds, fish, invertebrates
and fungi and provides an important source of epigenetic control for gene expression (Su et al.,
2011). In plants and animals, DNA methylation is known to be involved in diverse processes
including transposon silencing, X-chromosome inactivation, and imprinting (He et al., 2011). In
fungi, overall 5-mC content changes during development in Phymatotrichu omnivorum (Jupe et
al., 1986) and Magnaporthe oryzae (Jeon et al., 2015). Global patterns in DNA methylation has
also been shown to dramatically change in lichen fungi species when exposed to the algal
symbiont (Armaleo & Miao, 1999). Whole-genome bisulfite sequencing (WGBS) in
Ascomycetes has revealed that this group of fungi present repeated loci silenced by methylation
but also active genes emthylated within exonic regions. Zemach et al. (2010) reported a
correlation between gene body methylation and gene expression levels in Uncinocarpus reesii.
More recently, Jeon et al. (2015) ascribed a developmental role for DNA methylation in M.
oryzae. Recent years have seen a dramatic increase in the depth of understanding of how
epigenetic control mechanisms operate during plant/pathogen interactions (Boyko & Kovalchuk,
2011; Weiberg et al., 2013). However, little is known of the possible role played by DNA
methylation in moderating virulence in fungal plant-pathogen interactions or in its potential role
in mediating culture-induced loss of virulence. Here, we explore possible links between erosion
of pathogenicity from B. cinerea during protracted in vitro culture and dynamics of DNA
methylation across their genomes. We used Methylation Sensitive Amplified Polymorphisms
(MSAPs) (Reyna-López et al., 1997) to provide a preliminary survey of methylome flux
associated with gradual reduction in B. cinerea pathogenicity with time culture. We next sought
to identify Differentially Methylated Regions (DMRs) associated with progressive loss of
pathogenicity during in vitro culture using WGBS.
Materials and Methods
Botrytis cinerea culture and inoculation
bioRxiv preprint first posted online Jun. 17, 2016; doi: http://dx.doi.org/10.1101/059477. The copyright holder for this preprint (which was not
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Seven Botrytis cinerea cultures (IMI169558 isolate (Thomma et al., 1999)) were initiated from a
single frozen inoculum and cultured and harvested for 32 weeks as described in Johnson et al.
(2007). After 4 weeks in culture (T0) the initial culture was subcultured to 7 plates containing
fresh medium. Mycelium from each plate was subsequently subcultured every 4 weeks (1 month
hereafter) into fresh medium. A mycelium sample was taken from all replicates for DNA
extraction and conidia harvested from five replicates at every subculture for virulence analysis.
Finally, after the T8 challenge, B. cinerea was isolated from the infected areas, cultured and
immediately used to challenge A. thaliana plants to test virulence recovery (Figure S1) (T8P).
Plant material
A. thaliana Col-0 seeds were obtained from the Nottingham Arabidopsis Stock Centre (NASC)
and cultivated in Levington Universal compost in trays with 24-compartment inserts (Johnson et
al., 2003). Plants were maintained in Conviron (Controlled Environments Ltd) growth rooms at
24°C with a light intensity of 110 µmol m-2/s and an 8 h photoperiod for 4 weeks. For ease of
treatment, plants were transferred to Polysec growth rooms (Polysec Cold Rooms Ltd, UK;
http://www.polysec.co.uk/), maintained under the same conditions.
DNA isolation
126 B. cinerea genomic DNA (gDNA) extractions (comprised of 2 mycelium samples of each of
the 7 plates at in vitro time point (T1-T8) and from of the last time point culture transplanted
onto to A. thaliana (T8P)) were performed using the DNeasy 96 Plant Kit (Qiagen, Valencia,
CA) according to manufacturer’s instructions. Isolated DNA was diluted in nanopure water to
produce working stocks of 10 ng.µl-1. DNA from B. cinerea inoculated A. thaliana was extracted
from five leaf samples at each time point using DNeasy Mini Kit (Qiagen, Valencia, CA) as
above. DNA samples were diluted to 1 ng.µl-1.
Scoring B. cinerea lesion phenotypes
For assessments of infection phenotypes, leaves from five A. thaliana Col-0 plants (leaf stage 7/
8 as defined by Boyes et al. (2001), were inoculated on the adaxial surface with 5 µl of spore
suspension collected at each subculture time (T0-T8P). Controls were inoculated with PDB.
Disease lesions were assessed 3 days post inoculation. A weighted scoring method was used to
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categorize lesion phenotypes (Lloyd et al., 2011). High virulence symptoms (water-soaking,
chlorosis, and spreading necrosis) were conferred a range of positive scores and the resistant
symptoms (necrosis limited to inoculation site) were given negative scores (Figure 1A). A
weighted score was produced arithmetically from the lesion scores of replicates. Inoculated A.
thaliana leaves at T0, T8 and T8P were collected 3 days after inoculation for estimation of in
planta fungal development by quantitative real-time PCR (qPCR) (Figure S1).
Estimation of in planta fungal growth by qPCR
Reaction mixtures for qPCRs (25 μl) were prepared by mixing 10 ng of DNA with 12.5 μl of
SYBR™ Green Mastermix (Applied Biosystems, UK) and primers (300 nM final concentration).
Arabidopsis and Botrytis primers (Table S1) generated a 131 bp amplicon of the Shaggy-kinaselike gene and a 58 bp amplicon of the Cutinase A gene respectively (Gachon & Saindrenan,
2004). qPCRs were carried out using a Bio-Rad ABI7300 thermocycler using the following
conditions: 15 min (95°C) followed by 50 cycles of 15 s (95°C), 30 s (58°C) and 1 min (72°C).
This was followed by a dissociation (melting curve), according to the software procedure. Serial
dilutions of pure genomic DNA from each species were used to trace a calibration curve, which
was used to quantify plant and fungal DNA in each sample. Results were expressed as the
CG11/iASK ratio of mock-inoculated samples.
MSAP procedure
We used MSAPs (Rodríguez López et al., 2012) to reveal global patterns of variability that
reflect the divergence in methylation and sequence mutations between B. cinerea samples. 14
samples per time point (T2 to T8P) were analysed (2 replicated DNA extractions per culture
plate). For each individual sample, 50ng of DNA were digested and ligated for 2 h at 37°C using
EcoRI (5U) and MspI or HpaII (1U) (New England Biolabs), 0.45 μM EcoRI adaptor, 4.5 μM
HpaII adaptor (Table S1 for all oligonucleotide sequences) and T4 DNA ligase (1U) (Sigma) in
11 μl total volume of 1X T4 DNA ligase buffer (Sigma), 1μl of 0.5M NaCl, supplemented with
0.5 μl at 1mg/ml of BSA. Enzymes were then inactivated by heating to 75°C for 15 min.
Restriction/ligation was followed by two successive rounds of PCR amplification. For
preselective amplification, 0.3 μl of the restriction/ligation products described above were
incubated in 12.5 μl volumes containing 1X Biomix (Bioline, London, UK) with 0.05 μl of
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Preamp EcoRI primer and 0.25 μl Preamp HpaII/MspI (both primers at 10 uM) supplemented
with 0.1 μl at 1mg/ml of BSA. PCR conditions were 2 min at 72°C followed by 30 cycles of
94°C for 30 s, 56°C for 30 s and 72°C for 2 min with a final extension step of 10 min at 72°C.
Selective PCRs were performed using 0.3 μl of preselective PCR product and the same reagents
as the preselective amplification but using FAM labelled selective primers (E2/H1). Cycling
conditions for selective PCR were as follows: 2 min (94°C), 13 cycles of 30 s (94°C), 30 s
(65°C, decreasing by 0.7°C each cycle), and 2 min (72°C), followed by 24 cycles of 30 s (94°C),
30 s (56°C), and 2 min (72°C), ending with 10 min (72°C). Fluorescently labelled MSAP
products were diluted 1:10 in nanopure sterile water and 1 μl was combined with 1 μl of
ROX/HiDi mix (50 μl ROX plus 1 ml of HiDiformamide, Applied Biosystems, USA). Samples
were heat-denatured at 95°C for 3–5 min and snap-cooled on ice for 2 min. Samples were
fractionated on an ABI PRISM 3100 at 3 kV for 22 s and at 15 kV for 45 min.
Analysis of genetic/epigenetic variability during time in culture using MSAP
MSAP profiles were visualized using GeneMapper Software v4 (Applied Biosystems, Foster
City, CA). A qualitative analysis was performed in which loci were scored as ‘‘present’’ (1) or
‘‘absent’’ (0) to create a presence/absence binary matrix. MSAP selected loci were limited to
amplicons in the size range 80-585bp to reduce potential for size homoplasy (Caballero et al.,
2008). Samples were grouped according to the cumulative period in culture at the time of
collection Samples collected after 2, 3, 4, 5, 6, 7 and 8 months were denoted T2, T3, T4, T5, T6,
T7, T8 and T8P respectively whereas those cultured for 8 months and then inoculated onto onto
A. thaliana were labelled T8P.
Similarity between MSAP profiles obtained from primer combination E2/H1 and both enzymes
(HpaII and MspI) was first visualized using Principal Coordinate Analysis (PCoA) (Gower,
1966) using GenAlex (v.6.4) (Peakall & Smouse, 2012). We then used Analysis of Molecular
Variance (AMOVA) (Excoffier et al., 1992) to evaluate the structure and degree of diversity
induced by different times in culture. Pairwise PhiPT (Michalakis & Excoffier, 1996)
comparisons between samples restricted with HpaII or MspI from each time point and the
samples after the first passage (2 months in culture, T2) were used to infer their overall level of
divergence with time in culture (i.e., the lower the PhiPT value between samples restricted using
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HpaII or MspI, the smaller the differentiation induced by culture and the same samples).
AMOVA was subsequently calculated using GenAlex (v.6.5) to test the significance of PhiPT
between populations (Michalakis & Excoffier, 1996), with the probability of non-differentiation
(PhiPT=0) being estimated over 9,999 random permutations.
Mantle test analysis was used to estimate the correlation between the calculated pairwise
molecular distances and the difference in virulence between culture time points. The level of
significance was estimated over 9,999 random permutations tests, as implemented in Genalex
v6.5.
Methylation analysis by WGBS
DNA from 9 biological replicates from culture of two culture ages (1, 8 months) and from 9
replicates of T8P were randomly selected for sequencing. Biological replicates were used to
generate 3 pooled samples per culture age. Bisulphite treatment was performed independently
from 50ng of genomic DNA of each pooled sample using the EZ DNA methylation-Gold™ Kit
(Zymo Research) according to the manufacturers’ instructions but adjusting the final column
purification elution volume to 10 µl. Following Bisulphite treatment, recovered DNA from each
pool was used to estimate yield using a NanoDrop 100 spectrophotometer using the RNA setting.
Bisulphite treated samples were then used to create a sequencing library using the EpiGnome™
Methyl-Seq Kit and Index PCR Primers (4-12) according to manufacturer’s instructions. In order
to provide a reference draft sequence for the alignment of the bisulphite treated DNA and to
detect any culture induced genetic mutations, 10 ng of native (non-bisulphite treated) DNA
extracted from cultures from time points 1 month (T1) and 8 month (T8) were sequenced and
compared to the reference B. cinerea B05.10 genome sequence. Libraries were prepared using
the EpiGnome™ Methyl-Seq Kit and Index PCR Primers (Epicentre) (1-2) according to
manufacturer’s instructions.
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Library yield was determined by Qubit dsDNA High Sensitivity Assay Kit. Agilent 2100
Bioanalyzer High-Sensitivity DNA Chip was used to assess library quality and determine
average insert size. Libraries were then pooled and sequenced on Illumina HiSeq2000 (Illumina
Inc., San Diego, CA) 100bp paired-end V3 chemistry. Data available at Sequence Read Archive
(PRJEB14930).
Sequence analysis and differential methylation analysis
Sequencing
reads
were
trimmed
to
remove
adaptors
using
TrimGalore!
(http://www.bioinformatics.babraham.ac.uk/projects/trim_galore) and Cutadapt (Martin, 2011).
Whole genome re-sequencing reads were aligned to the published genome from B. cinerea
B05.10 (Broad Institute’s B. cinerea Sequencing Project) using bowtie2 (Langmead & Salzberg,
2012) and variants were called using freebayes (Garrison & Marth, 2012) after filtering for
coverage greater than 10x and >30 variant quality and removal of multi-allelic variants, variants
with missing data in one sample and variants with an observed allele frequency less than 0.5
(Jeon et al., 2013). Variant categories were analysed using CooVar (Vergara et al., 2012),
bedtools (Quinlan & Hall, 2010) and custom scripts. Bisulfite treated libraries were mapped
using Bismark (Krueger and Andrews, 2011) and bowtie2, duplicates removed and methylation
calls extracted using samtools (Li et al., 2009) and in-house scripts (available upon resquest).
Bisulfite sequencing efficiency was calculated by aligning reads to the B. cinerea mitochondrial
genome scaffold (B05.10) and identifying non-bisulfite converted bases. Differentially
methylated regions were called using a sliding window approach described in swDMR
(https://code.google.com/p/swdmr/). The significance of the observed DMRs was determined
using a three sample Kruskal-Wallis (3 sample) test between T1, T8 and T8P.
Results
Pathogenicity analysis of Botrytis cinerea
Isolates from all culture time points produced lesions on A. thaliana leaves but they varied in
their severity (Figure 1A). There was a progressive decline in disease scores recorded over the
eight-month period (i.e. T0-T8) (Figure 1B), with loss of virulence becoming significant from 3
months (T3) onwards (T-Test P<0.05) (Figure 1B). The disease scores for the T8P challenge did
not differ significantly from those recorded at T0 culture time, indicating that virulence had
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
recovered following a single passage through a plant (Figure 1B). Conversely, T8 virulence
scores were significantly lower than those obtained from T0 to T5 and also than T8P (T-Test
P<0.05) (Figure 1B). Infected leaves with T0 and T8P cultures did not show significant
differences in fungal DNA content measured by qPCR (Figure 1C). However, both showed
significantly higher levels of fungal DNA (T-Test P<0.05) than those infected using T8 cultures
(Figure 1C).
Analysis of genetic and epigenetic variance during culture using MSAPs
MSAP profiles generated 74 scored loci across the 112 samples of eight B. cinerea culture times
used in this study (T2-T8P). Multivariate analysis revealed that the MSAP profiles of B. cinerea
became progressively more dissimilar to the first time point analysed (T2) with increasing
culture age (Figure 2). Both, PCoA (Figure 2A and C) and PhiPT values (Figure 2B) showed
higher levels of culture-induced variability when using HpaII than when using MspI. PCoA
shows that samples cultivated for 3, 4, 5, 6, and 7 months occupied intermediate Eigenspace
between samples cultivated for 2 and 8 months (Figure 2C). Furthermore, a partial recovery of
the MSAP profile was observed on samples cultured for 8 months after one fungal generation on
the host plant (T8P) (Figure 2C). Calculated PhiPT values between each time point and T2
samples show a progressive increase in MSAP profile distance with time in culture when
samples were restricted with both enzymes. Analysis of Molecular Variance (AMOVA) analysis
shows that the calculated PhiPT values were significantly different (P<0.05) between T2 and
time points T6, T7, T8 and T8P when using MspI and T7, T8 and T8P when using HpaII (Figure
2B). Mantle test analysis showed significant correlations between disease score differences
among culture times and pairwise PhiPT values from MSAP profiles generated using MspI (R² =
0.316; P=0.005) and HpaII (R² = 0.462; P=0.002) (Figure S2).
B. cinerea genome resequencing
Divergence in MSAP profiles may occur through changes in methylation or through genetic
mutation. However, the recovery of profile affinity towards that of the original inoculum after a
single passage on Arabidopsis is difficult to reconcile genetically, suggesting the majority of the
changes to MSAP profiles was caused by differences in DNA methylation rasther than sequence
mutations. We therefore sought to better characterise mutational changes using a genome-wide
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sequencing approach. DNA extractions from two time points (1 month (T1) and 8 months (T8) in
culture) were compared to the Broad Institute’s B. cinerea B05.10 reference genome sequence
and used to assess mutational change during culture. Both samples were sequenced to an average
depth of 37.47x (35.64x for the 1-month culture and 39.30x for the 8-month culture) with an
average of 80% of reference bases being sequenced to a depth greater than 10x. After filtering
we found 2,331 sequence variants after 8 months in culture, of which 1,030 (44%) were small
insertions and deletions (INDELs) and 1,301 (56%) SNPs. Of these, just 454 were located within
genes including: 251 synonymous variants, 198 non-synonymous mutations (193 causing
missense variations and 5 causing premature stop codons (non-sense mutations). An additional 5
variants that altered predicted splice junctions were also identified.
We next focused the search for variants within the sequence of 1,577 B. cinerea genes with
known function including: secondary metabolism, conidiation, sclerotium formation, mating and
fruit body development, apoptosis, housekeeping, signalling pathways (Amselem et al., 2011)
and virulence senso lato genes. The virulence sensu lato genes included: appressoriumassociated genes (Amselem et al., 2011), virulence sensu stricto genes (Choquer et al., 2007)
and plant cell wall disassembly genes (CAZyme genes) (Blanco-Ulate et al., 2014). We found
68/1,577 (4.3%) of the tested genes contained one or more variants between T1 and T8 (See
Table 1 and Table S2 for a comprehensive list of genes with variants).
Characterisation of DNA Methylation changes by WGBS
We next characterised genome-wide DNA methylation flux by conducting WGBS from
triplicated genomic DNA extractions from mycelia of two different culture ages (T1 and T8) and
eight month cultures after inoculation onto A. thaliana plant (T8P). This yielded 187.5 million
reads, ranging from 12.61 to 34.05Gbp of sequence per sample after quality filtering. Mapping
efficiency of each replicate ranged from 54.3 to 67.6%, resulting in samples that generated
between 33 and 55x coverage of the 42.66Mbp genome (Table S3). This is the highest genome
coverage achieved to date for any fungal species after bisulfite sequencing. For each sample, we
covered over 91-93% of all cytosines in the genome (Table S3), with all samples having at least
81% of cytosines covered by at least four sequencing reads, allowing methylation level of
individual sites to be estimated with reasonable confidence.
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
WGBS identified an average of 15,716,603 mC per sample, indicating an average methylation
level of 0.6% across the genome as a whole (Table S4). The most heavily methylated context
was CHH followed by CG and CHG (where H is A, C or T) (Table S4). Global levels of mC did
not significantly change with culture time, although methylation in the rarer CG and CHG
contexts increased significantly (T-test, p=0.0008 and 0.0018) between 1 and 8 months in culture
(Figure 3A). However, modest declines were apparent in both contexts on eight month old
cultures inoculated onto A. thaliana (T8P) (Figure 3A) they proved not significant ((T-test,
p>0.05). Analysis of local levels of DNA methylation across the largest B. cinerea contig
(Supercontig 1.1) showed that DNA methylation is unevenly distributed and locally clustered
(Figure 3B-D). Observed clustering patterns were similar in all analysed samples (Figure 3B-D).
We next investigated the fine-scale distribution of DNA methylation across genic and regulatory
regions by surveying the location and density of mCs on all B. cinerea genes (exons and introns),
and presumed promoter regions, defined here as 1.5 kb upstream of the Transcription Starting
Site (TSS). Among these, mC levels increased between 1,000 and 500bp upstream of TSS. This
was followed by a sharp decrease then and increase in methylation before and after the start of
the coding sequence respectively (Figure 4). When time points T1, T8 and T8P were compared
on this genomic context, methylation levels where higher on T8P samples (Figure 4), in parallel
with that observed at a whole-genome level (Figure 3A).
The same methylation density analysis was then carried out for five housekeeping genes in
Botrytis
(i.e.,
G3PDH
(BC1G_09523.1);
HSP60
(BC1G_09341.1);
Actin
(BC1G_08198.1 and BC1G_01381.1) and Beta Tubulin (BC1G_00122.1). All five loci
possessed low levels of DNA methylation in every context and there were no changes in DNA
methylation between culture time points (i.e., T1, T8 and T8P) (Data not shown).
In common with most genes, those encoding putative CAZymes, proteins secreted by B. cinerea
upon plant infection (Table S5) (Blanco-Ulate et al., 2014), contained more methylation in
regions immediately upstream of the TSS (Figure 5A). However, these genes showed higher
levels of methylation after 8 months in culture than at T1 and at T8P. The observed increase in
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methylation on T8 samples was primarily driven by increased methylation in the CHG and CG
(Figure 5B-C) contexts. In contrast, CHH sites (Figure 5D) showed highst levels of methylation
on the T8P samples; following the general trend observed both at a whole genome level (Figure
3A) and by all B. cinerea genes (Figure 4).
Detection of culture induced DMRs
We sought to identify DMRs between three culture times (T1, T8 and T8P) by comparing
methylation levels across the whole genome of all samples implementing swDMR sliding
window analysis. Analysis of DMR length distribution showed DMRs sizes ranging from 12 to
4994bp (Figure 6A). The sliding window approach identified 2,822 regions as being significantly
differentially methylated in one of the samples compared to the other two for all mCs (Table 2,
Table S6). Overall methylation levels of DMRs decreased in all contexts (CG, CHG and CHH)
as time in culture progressed (from T1 to T8) but this was followed by a recovery of DNA
methylation levels after 8 month cultures were inoculated onto A. thaliana (T8P) (Figure 6b).
However, it is worth noting that T8 showed a larger number of outlier DMRs (greater than two
standard deviations away from the mean) that exhibited significantly higher levels of methylation
than the average (Figure 6B).
When examined individually, variance in DMRs between samples comprised of two main
pattern types (Table 2, Table S6): 1. 57.3% of the detected DMRs showed a recovery pattern
inoculation on A. thaliana such that there was no difference between T1 and T8P samples but
methylation levels diverged significantly in T8 (T1=T8P< >T8 (FDR<0.01))). 2. The remaining
DMRs showed a non-recovery pattern (i.e., T1< >T8P (FDR<0.01)). Two subtypes where found
for DMRs showing a DNA methylation recovery pattern: 1. DMRs showing an increase in
methylation with time in culture (T1=T8P<T8 (26.82%) (Type 0 hereafter) and 2. DMRs
showing a decrease in methylation level with time in culture (T1=T8P>T8 (30.47%)) (Type 2).
Equally, non-recovery DMRs can be divided into two categories: 1. DMRs showing a decrease
in methylation level with time in culture and no change in methylation level following
inoculation on A. thaliana (T1>T8=T7P (16.02%)) (Type 1a) and 2. DMRs not showing changes
in methylation level during culture but an increase in methylation after inoculation on A. thaliana
(T1=T8<T8P (26.68%)) (Type 1b). Curiously, no DMRs were observed to show a progressive
bioRxiv preprint first posted online Jun. 17, 2016; doi: http://dx.doi.org/10.1101/059477. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
increase in methylation level with time in culture and no change in methylation level after
inoculation on A. thaliana (T1<T8=T8P).
The vast majority (84.5%) of detected DMRs overlapped with 3,055 genic regions in the B.
cinerea genome (Table 3) while 438 (15.5%) mapped to intergenic regions. Almost all of the
genes implicated (98%) included DMRs within the gene body itself, with a small majority
(53.9%) also overlapping with the promoter region (Table 3, Table S7). The same analyses were
carried out to detect DMRs for CG, CHG and CHH contexts, identifying 70, 82 and 1,248 DMRs
respectively for each context (Table 3, Table S7). Of these, 91.4% (CG), 89.0% (CHG) and
85.2% (CHH) overlapped with 68, 84 and 1,339 genes respectively (Table 3, Table S7).
Finally, we conducted a search for DMRs overlapping with 1,577 B. cinerea genes with known
targeted functions that was carried out in the resequencing section above. Of these, 478 genes
(30.3%) overlapped with one or more detected DMRs (See Table 1 and Table S8 for a
comprehensive list of genes overlapping with DMRs).
Discussion
Culture-induced changes to MSAP profiles and virulence are simultaneous and
reversible.
In accordance with previous reports (Akamatsu et al., 1999; Chu et al., 2002; Hatta et al.,
2002; Castro et al., 2003), virulence of B. cinerea cultures progressively decreased with culture
age, but recovered after one passage of in vivo infection on A. thaliana (Butt et al., 2006).
Concurrent with these changes, MSAP profiles showed a similar progressive increase in
deviation from the starter culture profiles consistent with previous reports of accumulative
genetic/methylome change for other species, when similarly exposed to prolonged periods of
culture (Rodríguez López et al., 2010). This accumulation of variation as culture progressed
showed a positive linear correlation with the observed changes in virulence. This accords with
reported high levels of somaclonal variability arising during in vitro growth of phytopathogenic
fungi, which seemingly depresses the level of virulence of the culture isolates (Dahmen et al.,
1983). Whilst the source of the increased variation during protracted culture could have a genetic
or epigenetic cause (both mutation and methylation perturb MSAP profiles), reversal of
bioRxiv preprint first posted online Jun. 17, 2016; doi: http://dx.doi.org/10.1101/059477. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
virulence phenotype after a single passage of the cultured fungus on A. thaliana (T8P) strongly
implies that a plastic, epigenetic mechanism is primarily responsible for driving the observed
decline in virulence. This is most easily explained if culture alters the methylation status of the
genome and this in turn perturbs the expression control of genes responsible for virulence. For
this hypothesis to hold, a particularly strong link should exist between changes in the
methylation status of genes known to be implicated in virulence and the disease symptoms
evoked by culture. Establishing such links requires a more precise description of both methylome
flux and mutation in culture.
Sequence variants do not explain loss of virulence during culture
Whole genome resequencing of six DNA samples taken at two time points (one month and eight
months) allowed us to monitor for mutational change during the extended period of B. cinerea
culture. This work uncovered 198 non-synonymous mutations within genes. For genes with
unknown function, it is difficult to implicate these changes as being involved in the loss of
virulence observed during culture. We therefore screened specifically for the appearance of
genetic variants among 1,577 B. cinerea. Of these, just eight genes (0.5%) included sequence
variants that were not silent, conservative missense or synonymous mutations. Of the 1184 genes
associated to virulence, just six (0.5%) included a sequence variant that could conceivably affect
the virulence phenotype. All six were plant cell wall disassembly genes, CAZyme genes
(Lombard et al., 2014). It is tempting to speculate that some of the loss of virulence may have
been casually linked to one or more of these loss-of-function mutants being favored in vitro such
that virulence was progressively eroded but later restored by strong selection on the plant for
functioning alleles. However, there is extensive redundancy among the 275 CAZyme genes
present in the Botrytis genome (Blanco-Ullate et al., 2014) and it is highly unlikely that the
gradual loss of one or a small number of these genes would have such a pronounced effect on the
disease phenotype. Equally, it is difficult to reconcile the stochastic and infrequent nature of
mutation against the consistency in the timing and extent of virulence erosion/restoration seen
across all replicates. Thus, we are inclined not to concur with the view of previous studies for
other species that genetic mutation is the primary cause of the erosion of virulence during in vitro
culture of B. cinerea (Jeon et al., 2013).
bioRxiv preprint first posted online Jun. 17, 2016; doi: http://dx.doi.org/10.1101/059477. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
In vitro culture affects whole-genome methylation patterns
Our results suggest that changes in DNA methylation at a genome level are more likely than
mutation to be causally linked to the observed loss of virulence. To establish a more direct
association between methylome changes and gene activity we sequenced the methylomes of nine
samples from three culture time points (T1, T8 and T8P). All genomes presented similarly low
levels of DNA methylation (0.6%) as reported for other fungal species (Zemach et al., 2010).
DNA methylation was not evenly distributed across contigs but clustered following a mosaic
pattern (Feng et al., 2010), with higher levels of methylation in genomic regions with low gene
abundance. Jeon et al., (2015) noted similar methylation clustering patterns in pathogenic fungi
around gene-poor regions rich in transposable elements rich and found these to dynamically
follow fungal development. However, in our study clustering patterns were similar across time
points suggesting that the methylation changes detected here are unlikely to be associated to any
developmental progression that occurred during culture. However, global methylation levels
varied with sequence context (CHH>CG>CHG) and time in culture. In brief, methylation on
CGs and CHGs increased significantly between 1 (T1) and 8 (T8) months in culture. In both
sequence contexts, following inoculation on A. thaliana, T8P cultures lost much of the
methylation accumulated during culture and were no longer significantly different to the virgin
inoculums. This pattern of methylation flux concords with our MSAP results and indicates that
methylome variation accumulates in culture but is lost after contact with the host.
Previous genome-wide studies have shown that gene methylation in the monophyletic
Ascomycota phylum varies greatly. For example, in Neurospora crassa DNA methylation is not
found in gene bodies. However gene body methylation has been reported in other species such
as, Candida albicans, Uncinocarpus reesii and Magnaporthe oryza (Zemach et al., 2010; Su et
al., 2011; Jeon et al., 2015) while promoter methylation has only been reported in M. oryza (Jeon
et al., 2015). Analysis of the effect of methylation distribution and density on gene expression
has shown that gene body methylation has positive effects on gene expression (Zemach et al.,
2010; Jeon et al., 2015) while promoter methylation has a negative effect (Jeon et al., 2015). Our
analysis of methylation distribution in B. cinerea genes showed an increase in methylation
approximately 800bp upstream of the TSS followed by a sharp decrease at the TSS as shown by
Jeon et al (2015) in M. oryza mycelia. Our results showed a second increase in methylation
bioRxiv preprint first posted online Jun. 17, 2016; doi: http://dx.doi.org/10.1101/059477. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
density after the TSS indicating that at least some B. cinerea genes contain gene body
methylation.
Comparative analysis between culture time points showed that when all B. cinerea genes were
analysed collectively there were no changes in DNA methylation with time in culture. However,
while DNA methylation on house-keeping remained stable between culture time points,
CAZyme genes showed an increase in promoter methylation with time in culture. Moreover,
CAZyme genes showed a return to original methylation levels after a single passage on A.
thaliana (i.e. T1=T8P<T8). Conversely, all B. cinerea genes analysed collectively showed an
increase in methylation, similar to that observed at a whole genome scale (T1=T8<T8P). Global
methylation levels on the CAZyme gene in promoter regions showed a negative correlation with
virulence levels. This observed increase in promoter methylation on T8 samples seems to be due
to an increase on the CHG and CG contexts. Conversely, global methylation levels within
CAZyme gene bodies were positively correlated with virulence levels, largely due to changes in
the CHH context. The CAZyme gene family encodes proteins that breakdown, biosynthesise and
modify plant cell wall components and are highly expressed during plant invasion in B. cinerea
(Lombard et al., 2014; Blanco-Ulate et al., 2014). This gene family, should therefore, be readily
accessible to the transcriptional machinery in the virulent form of pathogenic fungi. Interestingly,
our results show an association between virulence levels in T1, T8 and T8P samples and a
methylation features in CAZyme genes that have positive effects on gene expression, i.e. high
gene-body methylation (Zemach et al., 2010; Jeon et al., 2015) and low promoter methylation
(Jeon et al., 2015) in high virulence cultures (T1 and T8P) and the opposite in the low virulence
culture (T8).
Culture induced DMRs are reversible and mirror changes in virulence
Overall, we identified 2,822 candidate DMRs between T1, T8 and T8P for all mCs, with DMRs
showing a decrease in methylation between T1 to T8 in all contexts (CG, CHG and CHH),
followed by a recovery of DNA methylation after culture on A. thaliana (T8P). Initially, this
seems to contradictobservations in global DNA methylation levels in which methylation
increased following the trend (T1<T8<T8P). This might simply suggest that there is a marked
difference between the behaviour of clustered and dispersed methylation. This assertion certainly
bioRxiv preprint first posted online Jun. 17, 2016; doi: http://dx.doi.org/10.1101/059477. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
concurs with similar differences noted between global ad local DNA methylation levels in
previous studies on different organisms (Jung & Pfeifer, 2015; Jeon et al., 2015). However, it
should be noted that T8 cultures exhibited a larger number of outlier DMRs showing containing
raised methylation levels, suggesting that not all predicted DMRs followed the same pattern of
demethylation followed by re-methylation. Furthermore, regional changes in DNA methylation
have been previously associated to the developmental potency of fungal cells (Jeon et al., 2015).
In their work, the authors showed how fungal totipotent cells (mycelia) possess higher global
methylation levels while cells determined to host penetration (appresoria) contain a higher
number of genes with methylated cytosines but lower global levels of DNA methylation. More
pertinently, 68.3% of the culture-induced DNA methylation changes (i.e. Type 0, 2 and 1a,
which accounts for 57.3% of the total detected DMRs) showed a recovery pattern after a single
round of inoculation on A. thaliana. This suggests that a majority of the DNA methylation
changes accumulated during in vitro culture are reset to their original state after a single passage
on the host. Viewed in this context, 26.7% of the observed changes (Type 1b) were not induced
by in vitro culture but by exposure to the host. This highlights the importance of epigenetic
mechanisms in regulatinghost/pathogen interactions (Gómez-Díaz et al., 2012; Weiberg et al.,
2013; Cheeseman & Weitzman, 2015), and the potential implication of dynamic DNA
methylation as regulator of phenotypic plasticity in response to nutrient availability and
interaction with the host (Mishra et al., 2011).
Of the detected DMRs for all mCs 84.5% overlapped with one or more genes suggesting that the
great majority of culture-induced DNA methylation flux occurs in genic regions. This overlap of
individual DMRs with one than more gene is probably partially due to the small average size of
intergenic regions (778 to 958bp) and genes (744 to 804 bp) in B. cinerea (Amselem et al.,
2011). Moreover, 98% of these overlapped, at least partially, with gene bodies. Taken
collectively, this indicates that B. cinerea genes in general and gene bodies in particular are
prone to environmentally-induced change to their methylation status. When DMRs were defined
using each DNA methylation context independently (i.e. CG, CHG and CHH), the vast majority
(89.1% of all DMRs and 89.9% of those overlapping with genes) occurred in the CHH context,
implying that DMRs in this context are the reason for the observed changes in detected CAZyme
gene body methylation associated with higher levels of virulence. Analysis of DMRs
bioRxiv preprint first posted online Jun. 17, 2016; doi: http://dx.doi.org/10.1101/059477. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
overlapping with genes with known function revealed that house-keeping genes did not overlap
with any DMRs while genes associated with apoptosis and conidiation showed the higher
percentage of overlapping DMRs (40.0 and 37.5% respectively). Remarkably, both biological
processes have been previously shown to be affected by DNA methylation (Belden et al., 2011;
Hervouet et al., 2013; Jeon et al., 2015). More importantly 27.6% of all virulence genes analyzed
overlapped with DMRs of which 77% showed recovery after a single passage on the host.
Our results imply that protracted culture of B. cinerea induces a hypermethylated, low
pathogenic form adapted to the host’s absence or the abundance of nutrients in the culture media.
It is tempting to speculate that the observed change in global and local levels of DNA
methylation during in vitro culture could be part of a mechanism that confers plasticity to the B.
cinerea genome to adapt to different environments. Butt et al., (2006) proposed that in vitro
culture induced loss of virulence could be the reflexion of an adaptive trait selected to promote
energy efficiency, by turning off virulence genes in the absence of the host or in nutrient rich
environments, and that such change could become maladaptive by restricting the pathogen to
saprophytism (Butt et al., 2006). Environmentally induced epigenetic adaptive changes have
been predicted to potentially induce evolutionary traps leading to maladaptation (Consuegra del
Olmo & Rodriguez Lopez, 2016). However, the complete reversibility of the reduced virulence
phenotype suggests that this might not be the case here.
We propose that the next step towards a better understanding of the epigenetic regulation of
virulence in fungi should be to confirm the causal links between the methylation changes
described here and perturbations in virulence genes expression. This, together with the increasing
availability of sequenced fungal genomes and methylomes, and our ability to decipher the
associations between changes in DNA methylation and virulence, will stimulate the
understanding of the mechanisms involved in fungal virulence control. Moreover, the analysis of
DMRs could potentially be used to predict gene function in non-model fungal species or even to
predict pathogenicity. More importantly, if the epigenetic regulation of the virulent/non-virulent
transition proposed here applies broadly to other pathogenic fungi, our findings will open the
door to a new type of non-lethal fungicide aimed at maintaining pathogenic fungi as saprotrophic
that would reduce the appearance of resistant strains.
bioRxiv preprint first posted online Jun. 17, 2016; doi: http://dx.doi.org/10.1101/059477. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
Author contributions:
J.B. did sequence data analysis and helped drafting the manuscript. A.S. cultured the Botrytis
samples and performed the virulence analysis. A.A. undertook the estimations of in planta fungal
develop using qPCR. M.W. and L.A.J.M. helped to conceive the study and drafting the
manuscript. C.M.R.L. conceived the study, carried out all nucleic acid extractions, performed
and analysed the MSAPs, performed and assisted analysing the native and bisulfite treated DNA
sequencing and wrote the manuscript. All authors have read and approved the final manuscript.
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FIGUREs AND TABLES
Figure 1: Estimation of Botrytis cinerea virulence on Arabidopsis thaliana. (a) Examples of
A. thaliana Col-0 leaves drop inoculated with B. cinerea and presenting disease scores ranging
from -0.5 (low virulence) to 0.5 (high virulence). (b) Estimated B. cinerea virulence at each time
culture point. Virulence of B. cinerea cultures was estimated over a period of eight months in
culture (T0, initial inoculum; T8, eight months in culture) and after 8 months in culture and a
single passage on A. thaliana (T8P). A weighted scoring method was used to categorize B.
cinerea lesion phenotypes 3 days post inoculation. Virulence symptoms (water-soaking,
chlorosis, and spreading necrosis) were conferred a range of positive scores and the resistant
symptoms (necrosis limited to inoculation site) were given negative scores. Asterisk symbols
under the horizontal axis indicate significant differences (*(T-Test; P<0.05) and ** (T-Test;
P<0.01)) between T0 and the time point over the asterisk. Asterisk symbols over the horizontal
axis indicate significant differences (** (T-Test; P<0.01)) between T8P and the time point under
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
the asterisk. (c) Detection of in planta B. cinerea hyphal mass in A. thaliana Col-0 by qPCR as
described by Gachon and Saindrenan, 2004.
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Figure 2: Effect of time in culture on genetic⁄epigenetic instability. (a, c) Principal coordinate
diagrams based on the Euclidian analysis of methylation-sensitive amplified polymorphisms
(MSAP) using enzymes HpaII (squares) and MspI (romboids) (a) and using enzyme HpaII
distances (c). 14 replicates from each time point are represented as red (T2: 2 months in culture),
orange (T3: 3 months in culture), yellow (T4: 4 months in culture), light green (T5: 5 months in
culture), dark green (T6: 6 months in culture), light blue (T7: 7 months in culture), dark blue (T8:
8 months in culture), and purple (T8P: 8 months+plant). The dashed line separates samples with
higher average levels of virulence from those of lower average levels of virulence. (b) Calculated
Pairwise PhiPT (Michalakis & Excoffier, 1996) comparisons between samples restricted with
HpaII (Blue) or MspI (Red) from each time point and the samples after the second passage (2
months in culture). * Indicates significantly different PhiPT values between T2 and the time
point under the asterix based on 10,000 permutations (P = 0.05).
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
Figure 3. Global changes on genomic distribution and levels of DNA methylation in B.
cinerea. A) Global average percentage of mCs ((number of mCs/total Cs)*100)) at each time
point (T1, red; T8, green and T8P, blue). B-D) Methylcytosines (mCs) density from each strand
(blue, positive and red, negative strand) across supercontig 1.1 at each time point (B=T1; C=T8
and D=T8P) was calculated and plotted as the percentage of mCs (as above) in each 10kb
window.
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Figure 4: Methylation density across transcription start sites (TSS) regions of all B. cinerea
selected genes. Methylation level (Vertical axis) at each time point (T1, red; T8, green and T8P,
blue) was identified as the proportion of methylated cytosines against all cytosines in 30bp
windows 1.5kb before and after the transcription start site.
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
Figure 5: Methylation density across transcription start sites (TSS) found in 131 genes
associated to polysaccharide degradation in B. cinerea (27). Methylation level (Vertical axis)
at each time point (T1, red; T8, green and T8P, blue) was identified as the proportion of
methylated cytosines against all cytosines in 30bp windows 1.5kb before and after the
transcription start site (TSS) (Horizontal axis). A) Methylation level for all mCs; B) Methylation
level for CpG context; C) Methylation level for CpHpG context and D) Methylation level for
CpHpH context.
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Figure 6: Analysis of in vitro culture induced DMRs in Botrytis cinerea. (a) Length
distribution of in vitro culture induced DMRs (i.e., regions presenting significantly different
methylation levels between one sample and the other two samples (FDR<0.01)) in Botrytis
cinerea. DRMs were determined using a three sample Kruskal-Wallis test. Methylation levels
were analysed by sliding window analysis using swDMR. (b) Methylation level distribution in in
vitro induced DMRs. Boxplot of 3 sample sliding window differential methylation analysis using
swDMR. The boxplots shows the distribution of each methylation in each context (CG, CHG and
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CHH) at three time points (T1, T8 and T8P), circles indicate DMRs with outlier levels of
methylation.
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
Table 1: Botrytis cinerea virulence genes with known function overlapping with in vitro
culture induced DMRs. Columns Gene Variant and Gene/DMR overlap indicate the number
(#) and percentage (%) of genes in each functional group with a genetic variant or overlapping
with a Differentially Methylated Region respectively. Column DMR type indicate how
methylation levels changed when comparing all three samples (T1, T8 and T8P). DMRs were
grouped according to their changing patterns into recovery (T1=T8P) and non-recovery (T1<
>T8P). Two subgroups where found for recovery (T1=T8P<T8 (Type 0) and T1=T8P > T8
(Type 2)) and non-recovery (T1>T8=T8P (Type 1a) and T1=T8<T8P (Type 1b)). Virul. s.l.
indicates virulence genes in abroad sense and include genes associated to:
Appressorium
formation, virulence on a strict sense and CAZyme genes.
Gene
Variant
Gene Function
Gene/DMR
overlap
DMR Type
Total
#G/D
%G/D
#G/D
%G/D
Recovery
0
2
Nonrecov
1a 1b
5
-
-
-
-
-
-
-
-
Apoptosis
10
2
20
4
40.0
3
1
-
-
Conidiation
16
-
-
6
37.5
1
2
-
3
Mating and fruit body
development
32
6
18.8
6
18.7
1
4
1
1
Secondary metabolism
51
4
7.8
14
27.5
9
3
2
3
Signalling pathways
176
6
3.4
54
30.7
12
17
7
20
Sclerotium formation
249
7
2.8
67
26.9
26
31
10
13
Appressorium
12
2
16.7
3
25.0
1
-
-
3
Virulence s.s.
17
1
5.9
4
23.5
1
1
1
2
1155
50
4.3
320
27.7
112
132
45
99
1577
68
4.3
478
30.3
166
191
66
144
Virul. s.l.
Housekeeping
CAZyme genes
Total
Reference
Amselem et al.,
2011
Amselem et al.,
2011
Amselem et al.,
2011
Amselem et al.,
2011
Amselem et al.,
2011
Amselem et al.,
2011
Amselem et al.,
2011
Amselem et al.,
2011
Choquer et al.,
2007
Blanco-Ulate et
al., 2014
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
Table 2: Number of Differentially Methylated Regions (DMRs) between T1, T8 and T8P
samples. DRMs (i.e., regions presenting significantly different methylation levels between one
sample and the other two samples (FDR<0.01)) were determined using a three sample KruskalWallis test. Methylation levels were analysed by sliding window analysis using swDMR. DMRs
were determined for all cytosines and for three methylation contexts. DMRs were grouped
according to their changing patterns into recovery (T1=T8P) and non-recovery (T1< > T8P).
Two subgroups where found for recovery (T1=T8P < T8 (Type 0) and T1=T8P > T8 (Type 2))
and non-recovery (T1>T8=T8P (Type 1a) and T1=T8<T8P (Type 1b)). Percentage of the total
DMRs for each pattern type/sequence context is shown in parenthesis.
Recovery (%)
Total
No recovery
Type 2
Total
recovery
(%)
757 (26.82)
860 (30.47)
1617 (57.30)
In vitro
induced
(Type 1a)
452 (16.02)
Type 0
Plant
induced
(Type 1b)
753 (26.68)
mC
2822
CG
70
17 (24.29)
15 (21.43)
32 (45.71)
14 (20.00)
24 (34.29)
CHG
82
14 (17.07)
30 (36.59)
44 (53.66)
15 (18.29)
23 (28.05)
CHH
1248
303 (24.28)
490 (39.26)
793 (63.54)
137 (10.98))
318 (25.48)
Total
4222
1395 (33.04)
1091 (25.84)
2486(58.88)
618 (14.64)
1118 (26.48)
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Table 3: Botrytis cinerea in vitro induced Differentially Methylated Regions overlapping
with genes. DRMs overlapping with genes (i.e., regions presenting significantly different
methylation levels between one sample and the other two samples (FDR<0.01)) were determined
using a three sample Kruskal-Wallis test. Methylation levels were analyzed by sliding window
analysis using swDMR. DMRs were determined for all cytosines and for three methylation
contexts. DMRs were grouped according to the genic region they overlapped with (i.e, Promoter,
promoter and Gene body, promoter, Gene body and 3'UTR, gene body and 3’UTR and gene
body). (**) Percentage of the total DMRs overlapping with each particular genic region. (*)
Percentage of the total number of genes showing a methylation recovery pattern.
Total genes
Promoter,
Gene body
and 3'UTR
(*)
Promoter
only (*)
Promoter
and Gene
body (*)
Gene body
and 3'UTR
(*)
Gene body
(*)
mC
3055
626 (20.49)
61 (2.00)
1022 (33.45)
923 (30.21)
423 (13.85)
CG
68
3 (4.41)
0 (0.00)
24 (35.29)
21 (30.88)
20 (29.41)
CHG
84
8 (9.52)
0 (0.00)
29 (34.52)
27 (32.14)
20 (23.81)
CHH
1339
248 (18.52)
32 (2.39)
443 (33.08)
413 (30.84)
203 (15.16)
Recovery
genes (**)
mC
1713 (56.07)
345 (20.14)
43 (2.51)
545 (31.82)
537 (31.35)
243 (14.19)
CG
32 (47.06)
2 (6.25)
0 (0.00)
11 (34.38)
9 (28.13)
10 (31.25)
CHG
46 (54.76)
4 (8.70)
1 (2.17)
13 (28.26)
16 (34.78)
12 (26.09)
CHH
863 (64.45)
175 (20.28)
21 (2.43)
286 (33.14)
252 (29.20)
129 (14.95)
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Supplementary Information
Fig. S1 Schematic representation of experimental design
Fig. S1 Seven replicated Botrytis cinerea cultures were initiated from a single frozen inoculum and
cultured for 36 weeks. All replicates were subcultured every 4 weeks to fresh culture medium and
mycelia were kept for DNA analysis (MSAPs, WGS, and BS-WGS). Conidia from five randomly selected
replicates (highlighted in red) at each time point were collected and used for virulence analysis by
inoculating five Arabidopsis thaliana. Inoculated A. thaliana leaves* at T1, T8 and T8P were collected 72
hours after inoculation for estimation of in planta fungal development by quantitative PCR. Conidia from
were collected from A. thaliana tissue infected with T8 fungus and were re-cultured for a short time to
generate conidia to test virulence recovery.
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
Fig. S2 Correlation between pairwise molecular differentiation and changes in virulence observed during
in vitro culture of B. cinerea.
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1
Fig. S2 Correlations were calculated using mantle test analysis on pairwise PhiPT values (GD)
2
(generated from MSAP data obtained using MspI (a) and HpaII (b)) and differences in virulence
3
between culture time points (VirD). Analysis using 1000 permutations showed significant
4
correlations (a: P = 0.005; b: P = 0.002).
5
6
41
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
7
Table S1 Sequences of oligonucleotide used for MSAP and qPCR.
8
Table S1 Click here to enter text.
Oligo name
Function
Sequence
iASK1
qPCR ASK forward primer
CTTATCGGATTTCTCTATGTTTGGC
iASK2
qPCR ASK reverse primer
GAGCTCCTGTTTATTTAACTTGTACATACC
CG11
qPCR CG1 forward primer
AGCCTTATGTCCCTTCCCTTG
CG12
qPCR CG1 reverse primer
GAAGAGAAATGGAAAATGGTGAG
Ad HpaII/MspI
MSAP Reverse Adaptor
GACGATGAGTCTAGAA
Ad. HpaII/MspI
MSAP Forward Adaptor
CGTTCT AGACTCATC
Ad. EcoRI
MSAP Reverse Adaptor
AATTGGTACGCAGTCTAC
Ad EcoRI
MSAP Forward Adaptor
CTCGTAGACTGCGTACC
Pre. EcoRI
MSAP Preselective primer
GACTGCGTACCAATTCA
Pre. HpaII/MspI
MSAP Preselective primer
GATGAGTCCTGAGCGGC
EcoRI2
MSAP Selective primer
GACTGCGTACCAATTCAAC
HpaII 2.1
MSAP Selective primer
GATGAGTCCTGAGCGGCA
9
10
42
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
11
Table S2 B. cinerea genes presenting sequence variants between 1 (T1) and 8 (T8) month old
12
cultures.
13
Table S2 + indicates the functional group the gene carrying the sequence variant belongs to. Some
14
genes present more than one sequence variant and can be present in more than one functional
15
group. Variant categories were analysed using CooVar, bedtools and custom scripts.
16
43
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
17
18
Table S3 Average coverage statistics for Whole Genome Bisulfite Sequenced samples.
19
Table S3 Average measurements were obtained from three replicates at each time point.
20
Sequencing reads from all samples were mapped to B. ciniera B05.10 genome using
21
Bismark/Bowtie2.
Sample
Mean
Coverage
Std dev
Cytosine
coverage
Cytosine
coverage (>4x)
mC (>1x)
T1
54.9939
20.3428
92.55%
87.65%
10.99%
T8
33.1991
13.868
91.50%
81.07%
9.14%
T8P
45.0209
17.0144
93.42%
86.67%
10.63%
22
23
44
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
24
Table S4 Genome mapping statistics from 9 bisulfite converted Botrytis samples.
25
Table S4 Sequencing reads from all samples were mapped to B. ciniera B05.10 genome using
26
Bismark/Bowtie2
Total methylated C's
Total non-methylated C's
% methylation
%mC
Samples
Total bp
% Map. Effic
CpG
CHG
CHH
CpG
CHG
CHH
CpG
CHG
CHH
T1BS1
26382612
67.6
477254
438983
2499975
130600424
111869938
391807290
0.36
0.39
0.63
0.54
T1BS2
34048060
62.9
526341
485477
2494454
155155501
134341879
485255169
0.34
0.36
0.51
0.45
T1BS3
17347608
61.1
306505
279816
1575956
77226256
66054702
232502707
0.40
0.42
0.67
0.57
AvT1
25926093.33
63.87
436700.00
401425.33
2190128.33
120994060.33
104088839.67
369855055.33
0.36
0.38
0.59
0.52
T8BS1
18543427
62
394348
360641
1568745
83351098
71894819
257263175
0.47
0.50
0.61
0.56
T8BS2
12613134
60.7
248968
229853
902624
55786373
47759672
166229028
0.44
0.48
0.54
0.51
T8BS3
17117307
65.1
391446
360065
2142019
79537475
69135859
257486354
0.49
0.52
0.83
0.71
AvT8
16091289.33
62.60
344920.67
316853.00
1537796.00
72891648.67
62930116.67
226992852.33
0.47
0.50
0.67
0.59
T8PBS1
28336888
62.3
524875
487874
3561078
126943754
110309124
404488048
0.41
0.44
0.87
0.71
T8PBS2
18471780
66.9
435528
403984
2932991
89352607
77332840
279892313
0.49
0.52
1.04
0.84
T8PBS3
14694194
54.3
220458
201667
924438
57483914
49293008
173794940
0.38
0.41
0.53
0.48
AvT8P
20500954.00
61.17
393620.33
364508.33
2472835.67
91260091.67
78978324.00
286058433.67
0.43
0.46
0.86
0.68
27
28
45
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
29
Table S5 Botrytis cinerea CAZYme genes selected for the analysis of methylation density shown on
30
Figure 5.
31
Table S5 (Modified from Blanco-Ulate et al 2014)
32
46
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
33
34
Table S6 In vitro culture and plant induced and differentially Methylated Regions (DMRs) in Botrytis
35
cinerea.
36
Table S6 DMRs were determined from whole genome bisulfite sequencing data using 3 sample
37
Kruskal-Wallis test on a sliding window analysis conducted through swDMR. Methylation levels
38
(%mC) were calculated as proportion of methylation (i.e., number of methylated Cs divided by the
39
total number of Cs) and were considered significantly different when the calculated FDR was lower
40
than 0.01. Three replicates were used per time point (T0, T8 and T8P, 2, 8 month in culture and 8
41
months and inoculation onto A. thaliana respectively). Samples were grouped according to four
42
patterns on DNA methylation behavior (2, T0=T8P > T8; 1a, T0>T8=T8P; 1b, T0=T8<T8P and 0,
43
T0=T8P < T8). Coverage indicates the calculated average sequencing coverage for each DMR.
44
47
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
45
Table S7 Botrytis cinerea in vitro induced Differentially Methylated Regions overlapping with genes.
46
Table S7 DRMs overlapping with genes (i.e., regions presenting significantly different methylation
47
levels between one sample and the other two samples (FDR<0.01)) were determined using a three
48
sample Kruskal-Wallis test. Methylation levels were analyzed by sliding window analysis using
49
swDMR. DMRs were determined for all context. DMRs were grouped according to the genic region
50
they overlapped with (i.e, Promoter, promoter and Gene body, promoter, Gene body and 3'UTR,
51
gene body and 3’UTR and gene body). Methylation pattern was considered as recovered when
52
methylation at T0 was equal (not significantly different) to T8P and lower or higher than that at T8.
53
48
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54
Table S8 B. cinerea genes with known function overlapping with in vitro culture induced DMRs.
55
Table S8 Colums mC, CG, CHG and CHH indicate what region of the gene overlaps with the DRM
56
and in what Cytosine context the DMR is observed (mC= all Cytosines; CG, CHG and CHH ). DRM type
57
indicates what DNA methylation behavior the DMR showed when comparing all three samples (2,
58
T1=T8P > T8; 1a, T1>T8=T8P; 1b, T1=T8<T8P and 0, T1=T8P < T8).
59
in vitro culture of B. cinerea.
49